Using CFD for Localized Weather Prediction for Real Time Wildfire Response.

On July 23, 2018, the Greek seaside town of Mati was transformed into the site of one of deadliest wildfires of the 21st Century. Within 90 minutes, strong winds accelerated a firestorm that raced from the hills of Penteli to the coastline, killing 104 people and forcing hundreds into the sea. The fire’s unprecedented speed, fueled by 40 °C heat, extreme dryness, and gusts reaching 124 km/h, exposed devastating gaps in evacuation planning, early warnings, and real-time situational awareness.

As wildfires grow more intense and unpredictable worldwide, events like Mati serve as stark reminders of what’s at stake and what can be improved. One of the most powerful tools emerging from this urgency is localized weather prediction powered by Computational Fluid Dynamics (CFD).

Total area burnt by the wildfire.

The Role of Localized Weather Prediction in Variable Weather Conditions

Localized weather prediction goes beyond standard forecasts. By simulating how wind behaves in specific terrains accounting for buildings, vegetation, and microclimates it enables a more accurate assessment of fire spread risk. For fire-prone areas, this means earlier warnings, safer evacuation planning, and more informed firefighting deployment.

Traditional fire response plans often rely on regional data. But fires like Mati unfold on a hyperlocal level. Had responders and planners had access to localized, weather forecasts, they may have better predicted the fire’s path, identified critical escape zones, and issued timely evacuation orders.

Why localised weather forecasts matter?

Localised weather forecasts use fluid dynamics modeling to replicate how wind behaves across complex surfaces. Tools like Archiwind allow urban planners, emergency responders, and insurance analysts to visualize wind speed, direction, and turbulence at street-level resolution.

This isn’t just theoretical science. At Nablaflow we have been using CFD simulations already to predict and to plan safe pedestrian zones in high rise cities and forecast site-level safety during typhoons (Typhoon Shanshan). In wildfire scenarios, it can become a tool to use to predict fire spread through mapping the wind patterns that drive fire behavior before they unfold in real time.

Actual Simulation of Hyperlocal Weather Prediction in a construction site before Typhoon Shanshan hit.

From Prediction to Protection: Supporting Firefighters and Evacuation Planning

Firefighters needed to take fast decisions, often in terrain where conditions shift by the minute. With weather forecast software informed by real-time weather data and CFD analysis, response teams can prioritize safety zones, anticipate wind reversals, and plan safer approach routes.

For evacuation planning, localized simulations can show how wind and smoke might affect visibility or access along different routes. This improves routing decisions and helps prevent deadly bottlenecks like those witnessed in Mati’s narrow coastal roads.

Even beyond firefighting, insurance companies can use CFD data to improve climate risk technology models, understanding which neighborhoods face the highest urban wildfire risk, and designing better mitigation and coverage strategies.

Case Study Insights: Learning from the 2018 Mati Fires

In Mati, the speed of the fire left no room for improvisation. Emergency services struggled to coordinate amidst rapidly deteriorating conditions. And critically, residents didn’t know when or where to flee.

Complicating matters, Greece’s emergency resources were already divided. At the same time the fire ignited near Penteli, another large area was burning near Kineta, demanding parallel attention and fire suppression efforts. With limited manpower and equipment, first responders found themselves in an impossible situation managing two active fronts under extreme conditions.

As the Mati fire accelerated, emergency responders, including police and firefighters, were forced to make split-second decisions on simultaneous firefighting and evacuation. The lack of predictive tools left them reactive instead of proactive. Many victims remained unaware of the threat until it was too late.

With tools like Archiwind, planners could have run local weather simulations days in advance. On the day of the fire, real-time prediction software could have updated forecasts as wind gusts intensified. Early warning fire systems, powered by CFD, may have identified risk zones hours before the fire reached homes.

How Technology Like Archiwind Enhances Fire Response

Archiwind isn’t a weather app it’s a simulation tool . By integrating live weather data with terrain mapping and computer fluid dynamics (CFD), it offers real-time forecasts that go into great detail. While not a dedicated wildfire management platform, its current capabilities are aligned with the foundational needs of:

• Live wind mapping to anticipate fire spread
• Potential CFD fire modeling in urban areas
• Setting automated alert thresholds for emergency protocols
• Integration into climate risk technology platforms for insurers and municipalities.

As climate change intensifies, wildfire seasons grow longer and more unpredictable. But with the right tools, we can shift from reactive to proactive

Hyperlocal wind speed prediction using Archiwind Live feature

Conclusion

Wildfires like the the ones in Mati in 2018 remind us of the urgency for smarter planning, faster warnings, and data-driven decision-making. Local agencies faced a dual crisis: fires in both Kineta and Mati, with emergency forces stretched thin and caught off guard by the fire’s explosive speed. Localized weather prediction, powered by CFD, offers a transformational edge for emergency response and urban resilience.

At Nablaflow, we believe that technology like Archiwind can play a critical role in protecting lives and communities. Whether it’s planning evacuations, modeling fire spread, or building smarter insurance strategies, the future of wildfire management must be predictive, localized, and deeply informed.